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Special Issue Editors

Dr. Feng Xu

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Guest Editor

Shenzhen International Graduate School, Tsinghua University, Shenzhen 518055, China
Interests: fault diagnosis; state estimation; fault-tolerant control; set theory; model predictive control; fuzzy control; sliding mode control; data-driven control; reinforcement learning

Prof. Dr. Fanglai Zhu

E-Mail Website
Guest Editor

College of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Interests: unknown input observer design; disturbance observer design; fault diagnosis; fault detection and fault-tolerant control; security control for CPS; security state estimation; cooperative control for multi-agent system; attack detection for CPS and MAS; T-S fuzzy model control; sliding mode robust control
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Special Issue Information

Dear Colleagues,

State estimation and fault diagnosis play important roles in control systems. Since technical systems are always affected by uncertainties such as disturbances and noise, the extensive presence of uncertainties deteriorates state estimation and fault diagnosis performances, which further deteriorates the control system performance and safety. This situation motivates us to handle the effect of uncertainties on state estimation and fault diagnosis, i.e., the so-called robustness of state estimation and fault diagnosis. However, it does not mean that the stronger the robustness is, the better the performances are. It is important to balance robustness and other state estimation and fault diagnosis performances and optimize state estimation and fault diagnosis methods. Additionally, both state estimation and fault diagnosis depend on system input and output information to estimate states and make fault diagnosis decisions. However, the acquisition of system information relies on different types of sensors. How to make use of information acquired by sensors, how to place sensors in systems, and how to diagnose faults in sensors are key to state estimation and fault diagnosis, especially for large-scale systems, networked/distributed systems, etc. In this Special Issue, we are interested in novel state estimation and fault diagnosis methods and related works to improve state estimation and fault diagnosis performances. This Special Issue focuses on but is not limited to the following topics:

Observer design and fault diagnosis for uncertain systems.
State estimation and fault diagnosis for large-scale systems.
Optimization of robust state estimation and fault diagnosis methods.
Input design for active fault diagnosis.
Data-driven state estimation and fault diagnosis.
Reinforcement learning for active fault diagnosis.
Sensor placement for state estimation and fault diagnosis.
Sensor information fusion for state estimation and fault diagnosis.

Dr. Feng Xu
Prof. Dr. Fanglai Zhu
Guest Editors

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Published Papers (2 papers)

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Research

19 pages, 752 KiB

Open AccessArticle

Event-Triggered Robust State Estimation for Nonlinear Networked Systems with Measurement Delays against DoS Attacks

by Min Wang and Huabo Liu

Sensors 2023, 23(14), 6553; https://doi.org/10.3390/s23146553 - 20 Jul 2023

Cited by 1 | Viewed by 703

Abstract

In this paper, we focus on the event-triggered robust state estimation problems for nonlinear networked systems with constant measurement delays against denial-of-service (DoS) attacks. The computation of the extended Kalman filter (EKF) generates errors of linearization approximations, which can result in increased state estimation errors, and subsequently amplifies the linearization errors. DoS attacks interfere with the transmission of measurements sent to the remote robust state estimator by overloading the communication networks, while the communication rate of the communication channel is constrained. Therefore, an event-triggered robust state estimation algorithm based on sensitivity penalization with an explicit packet arrival parameter is derived to defend against DoS attacks and linearization errors. Meanwhile, the presence of measurement delays precludes the direct use of conventional state estimation algorithms, prompting us to devise an innovative state augmentation method. The results of the numerical simulations show that the proposed robust state estimator can appreciably improve the accuracy of state estimation. Full article

(This article belongs to the Special Issue Robust State Estimation and Fault Diagnosis of Dynamic Systems)

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22 pages, 2028 KiB

Open AccessArticle

RAIM and Failure Mode Slope: Effects of Increased Number of Measurements and Number of Faults

by Jean-Bernard Uwineza and Jay A. Farrell

Sensors 2023, 23(10), 4947; https://doi.org/10.3390/s23104947 - 21 May 2023

Viewed by 915

Abstract

This article provides a comprehensive analysis of the impact of the increasing number of measurements and the possible increase in the number of faults in multi-constellation Global Navigation Satellite System (GNSS) Receiver Autonomous Integrity Monitoring (RAIM). Residual-based fault detection and integrity monitoring techniques are ubiquitous in linear over-determined sensing systems. An important application is RAIM, as used in multi-constellation GNSS-based positioning. This is a field in which the number of measurements, m, available per epoch is rapidly increasing due to new satellite systems and modernization. Spoofing, multipath, and non-line of sight signals could potentially affect a large number of these signals. This article fully characterizes the impact of measurement faults on the estimation (i.e., position) error, the residual, and their ratio (i.e., the failure mode slope) by analyzing the range space of the measurement matrix and its orthogonal complement. For any fault scenario affecting h measurements, the eigenvalue problem that defines the worst-case fault is expressed and analyzed in terms of these orthogonal subspaces, which enables further analysis. For

h > (m - n)

, where n is the number of estimated variables, it is known that there always exist faults that are undetectable from the residual vector, yielding an infinite value for the failure mode slope. This article uses the range space and its complement to explain: (1) why, for fixed h and n, the failure mode slope decreases with m; (2) why, for a fixed n and m, the failure mode slope increases toward infinity as h increases; (3) why a failure mode slope can become infinite for